A Knowledge Management Agent handling 1,000 monthly interactions creates a net value of $4,458 per month when achieving a 65% success rate. This value is driven by saving 108 hours of net staff time, even after accounting for the 35% of cases that require human escalation and the 2 minutes of review required for every successful outcome.
The true cost of the agent is $2.31 per successful outcome. This calculation moves beyond simple license fees to include the platform cost and the loaded staff cost of $55 per hour for oversight. Tracking these variables ensures the agent is actually reducing the workload rather than shifting it to different manual tasks.
Agent ROI worked example
Worked example for Knowledge Management Agent using stated NetLift assumptions:
| Input (stated assumption) |
Value |
| Interactions handled per month |
1,000 |
| Accepted / successful outcomes |
65% |
| Escalated to a person |
35% |
| Staff minutes saved per accepted outcome |
12 min |
| Human review per accepted outcome |
2 min |
| Loaded staff cost |
$55/hour |
| Agent platform cost per month |
$1,500 (stated assumption) |
| Computed result |
Value |
| Successful outcomes per month |
650 |
| Cost per successful outcome |
$2.31 |
| Net staff time saved |
108 h / month |
| Labour value of time saved |
$5,958 / month |
| Current net value |
$4,458 / month |
Escalation, review and rework are part of the true cost of an AI agent. Track them — an agent that resolves fewer tickets with less rework can beat one that closes more tickets badly.
What determines the cost per successful outcome?
The cost per outcome is calculated by dividing the monthly platform cost by the number of interactions that do not require escalation. In this model, with a $1,500 monthly cost and 650 successful outcomes, each success costs $2.31. To maintain this efficiency, the agent must deliver a high enough success rate to offset the $55 hourly loaded cost of the staff members who handle the remaining 35% of escalated queries.
Why must we track human review and rework?
Review and rework are part of the true cost of an AI agent. In this scenario, every accepted outcome requires 2 minutes of human review. If review time increases or the success rate drops, the net value of $4,458 per month will erode. Monitoring these metrics allows leaders to decide whether to Expand, Continue, or Improve the deployment based on whether the agent resolves tickets with less rework than a human baseline.
NetLift measures the deterministic value of AI adoption by comparing the time work takes with AI against a manual baseline. Instead of relying on hype, we grade Evidence Quality from Estimate to Verified and assign every agent a decision state like Expand or Stop. By calculating the current net value—labor value of time saved minus the full platform and review costs—NetLift provides a clear financial signal for KM agent renewals.